Cloudera

JUNE 29, 2023

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. Use cases include: Enable transparent access to financial data. Possible applications include: Improved customer risk profiling.

Data Architecture 85

Data Architecture Architecture Management Banking 85

ProjectPro

FEBRUARY 8, 2023

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Create schedules or events that will act as job triggers.

AWS 98

AWS Scala Metadata Data Lake 98

Lyft Engineering

NOVEMBER 29, 2023

Our initial use for Druid was for near real-time geospatial querying and high performance on high-cardinality data sets. It also allowed us to optimize for handling time-series data and event data at scale. Pre-aggregating data at ingestion time helped optimize our query performance and reduce our storage costs.

Kafka 107

Kafka Data Ingestion Architecture Datasets 107

Cloudera

NOVEMBER 13, 2020

The reality is that data warehousing contains a large variety of queries both small and large; there are many circumstances where Impala queries small amounts of data; when end users are iterating on a use case, filtering down to a specific time window, working with dimension tables, or pre-aggregated data.

Metadata 143

Metadata Coding SQL Database 143

Confluent

FEBRUARY 6, 2019

The data scientist “just” deploys its trained model, and production engineers can access it. While all these solutions help data scientists, data engineers and production engineers to work better together, there are underlying challenges within the hidden debts: Data collection (i.e.,

Machine Learning 107

Machine Learning Python Kafka Java 107

Netflix Tech

JULY 12, 2019

Gatekeeper accomplishes its prescribed task by aggregating data from multiple upstream systems, applying some business logic, then producing an output detailing the status of each video in each country. Near : the cache exists in RAM on any instance which requires access to the dataset.

Datasets 73

Datasets Kafka Architecture Computer Science 73

Snowflake

JUNE 8, 2023

Data producers deliver data products from a single source or set of sources, such as data from a CRM application. Those data products could be used by themselves or aggregated into an aggregate data product, like the customer 360 described above. Product thinking works from the outside in.

Aggregated Data 72

Aggregated Data Raw Data Data Telecommunication 72

LinkedIn Engineering

DECEMBER 19, 2023

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. Accessibility of all namenodes. No concurrent upgrades are happening within the cluster.

Big Data 92

Big Data Hadoop Metadata Data 92

DoorDash Engineering

APRIL 12, 2023

Additionally, having a centralized repository of metrics facilitates easy access to metrics, empowering all members of an organization to analyze experiment results. Data scientists are the primary metric creators and are already familiar with SQL, so it made sense to use SQL as the language to define metrics instead of building our own DSL.

SQL 83

SQL Metadata Raw Data Government 83

Rockset

AUGUST 25, 2021

It’s simply too expensive to store all the raw data and simply too slow to run batch processes to pre-aggregate it. One common example is a mobile app, where every activity is recorded as an event, resulting in millions of events per day streaming in.

Aggregated Data 52

Aggregated Data Hadoop SQL Data Lake 52

AltexSoft

OCTOBER 21, 2022

This scenario involves three main characters — publishers, subscribers, and a message or event broker. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka cluster and brokers.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Rockset

AUGUST 30, 2021

It eliminates the cost and complexity around data preparation, performance tuning and operations, helping to accelerate the movement from batch to real-time analytics. The latest Rockset release, SQL-based rollups, has made real-time analytics on streaming data a lot more affordable and accessible.

SQL 52

SQL Kafka MongoDB MySQL 52

DoorDash Engineering

OCTOBER 17, 2023

Experiment exposures are one of our highest volume events. On a typical day, our platform produces between 80 billion and 110 billion exposure events. We stream these events to Kafka and then store them in Snowflake. Users can query this data to troubleshoot their experiments. Below are sample charts from our dashboards.

Education 87

Education Kafka Algorithm Data Warehouse 87

Picnic Engineering

OCTOBER 31, 2023

Datadog aggregates data based on the specific “operations” they are associated with, such as acting as a server, client, RabbitMQ interaction, database query, or various methods. The capability to aggregate data in one place, combined with a wide range of integrations, simplifies data collection and access.

Java 52

Java Aggregated Data Coding Python 52

Striim

AUGUST 22, 2024

Then, the data is analyzed and transformed into a format that’s digestible for target users, enabling them to draw conclusions or immediately garner insights once the data is entered into a company’s system. Users can access this data on a dashboard, report, or another medium. Cloud Solutions Architect at Striim.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

Rockset

MARCH 1, 2023

In addition, Rockset provides fast data access through the use of more performant hot storage, while cloud storage is used for durability. Then, I used the integration to create a collection, twitter_kinesis_30day , that syncs Twitter data from the Kinesis stream.

Architecture 59

Architecture AWS SQL Cloud Storage 59

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Event streams.

IT 59

IT Data Warehouse Data Governance Data Lake 59

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. In the event that they are not the same, what are the difference s? The DW and databases support multi-user access. In order to access the data, both the DW and the database require queries.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Striim

OCTOBER 11, 2024

Data pipelines are significant to businesses because they: Consolidate Data: Data pipelines are responsible for integrating and unifying data from diverse sources and formats, making it consistent and usable for analytics and business intelligence.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Knowledge Hut

JANUARY 19, 2024

New Analytics Strategy vs. Existing Analytics Strategy Business Intelligence is concerned with aggregated data collected from various sources (like databases) and analyzed for insights about a business' performance. Ease of Operations BI systems make it easy for businesses to store, access and analyze data.

Business Intelligence 52

Business Intelligence BI Business Analyst Aggregated Data 52

Rockset

NOVEMBER 1, 2022

Step 1: Data Acquisition Elasticsearch is rarely the system of record which means the data in it comes from somewhere else for real-time analytics. Rockset has built-in connectors to stream real-time data for testing and simulating production workloads including Apache Kafka , Kinesis and Event Hubs.

Database-centric 52

Database-centric SQL Pipeline-centric Aggregated Data 52

DareData

JULY 5, 2023

The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format. Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

ProjectPro

OCTOBER 28, 2015

The aim of selecting an ETL tool is to ensure that data is moving into Hadoop at a frequency that can meet the analytic requirements. Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization What is Sqoop in Hadoop?

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Knowledge Hut

OCTOBER 13, 2023

Aggregate Data: If you don't need granularity, consider aggregating data before loading it into Power BI to reduce the volume of data. Sort and Filter Early: Apply sorting and filtering in your queries as early as possible to reduce the amount of data transferred and processed.

BI 52

BI Business Analyst Datasets Raw Data 52

Airbnb Tech

APRIL 30, 2021

Minerva takes fact and dimension tables as inputs, performs data denormalization, and serves the aggregated data to downstream applications. Flexible Backfills: Minerva version controls data definitions, so major changes to the datasets are automatically tracked and backfilled.

Data Warehouse 52

Data Warehouse Finance Metadata Aggregated Data 52

Knowledge Hut

JANUARY 3, 2024

After thoroughly examining each circumstance, they will share any new knowledge obtained about current cyber threats or vulnerabilities inside their network so that future events can be avoided, if possible, by immediate fixes. Essential compliance consists of access to patch levels, threat information, identity, and data access control.

Computer Science 52

Computer Science Certification Data Mining Data Security 52

ProjectPro

JANUARY 25, 2022

PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization The PySpark Architecture The PySpark architecture consists of various parts such as Spark Conf, RDDs, Spark Context, Dataframes , etc.

Big Data 52

Big Data Data Process Process Kafka 52

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Databand.ai

JULY 10, 2023

This includes integration with common data sources, incident management systems, ticketing systems, CI/CD tools, and more, further streamlining the process of identifying and resolving issues. Security: Observability platforms often include built-in security features to ensure the integrity and confidentiality of your data.

Data Pipeline 52

Data Pipeline Algorithm Data Engineering Raw Data 52

LinkedIn Engineering

DECEMBER 20, 2023

This includes taking measures such as issuing warnings, restricting access, or suspending accounts as necessary. The feedback loop serves as a critical component of a dynamic defense strategy, constantly monitoring and aggregating data from abuse reports, member feedback, and reviewer input.

Building 91

Building Algorithm Kafka Machine Learning 91

ProjectPro

DECEMBER 7, 2021

The second step for building etl pipelines is data transformation, which entails converting the raw data into the format required by the end-application. The transformed data is then placed into the destination data warehouse or data lake. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

phData: Data Engineering

JANUARY 3, 2022

The big data analytics market is set to reach $103 billion by 2023 , with poor data quality costing the US economy up to $3.1 Fortune 1000 companies can gain more than $65 million additional net income, only by increasing their data accessibility by 10%. What is Data Governance? trillion yearly.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Scott Logic

JULY 26, 2023

Knowledge Graphs, to quote the Alan Turing Institute , “organise data from multiple sources, capture information about entities of interest in a given domain or task (like people, places or events), and forge connections between them.” It was hypothesised that in combination with the Knowledge Graph, the LLM (e.g.

Banking 52

Banking Aggregated Data Retail Architecture 52

AltexSoft

DECEMBER 23, 2022

In ELT, raw data is loaded into the destination, and then it receives transformations when it’s needed. Organizations now operate huge amounts of various data stored in multiple systems. ELT makes it easier to manage and access all this information by allowing both raw and cleaned data to be loaded and stored for further analysis.

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

NOVEMBER 15, 2021

When any particular project is open-sourced, it makes the source code accessible to anyone. The adaptability and technical superiority of such open-source big data projects make them stand out for community use. It serves as a distributed processing engine for both categories of data streams: unbounded and bounded.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

JUNE 28, 2016

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization What Kafka does? Kafka is extensively being used across industries for general – purpose messaging system where high availability and real time data integration and analytics are of utmost importance.

Kafka 40

Kafka Systems Hadoop Big Data 40

Knowledge Hut

NOVEMBER 17, 2023

To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases. You should be able to create intricate queries that use subqueries, join numerous tables, and aggregate data.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52